In this paper we explore the use of non-linear transformations in
order to improve the performance of an entropy based voice activity detector
(VAD). The idea of using a non-linear transformation comes from some
previous work done in speech linear prediction (LPC) field based in source
separation techniques, where the score function was added into the classical
equations in order to take into account the real distribution of the signal. We
explore the possibility of estimating the entropy of frames after calculating its
score function, instead of using original frames. We observe that if signal is
clean, estimated entropy is essentially the same; but if signal is noisy
transformed frames (with score function) are able to give different entropy if
the frame is voiced against unvoiced ones. Experimental results show that this
fact permits to detect voice activity under high noise, where simple entropy
method fails.